Citation

Alser O, Dorken-Gallastegi A, Proaño-Zamudio JA, Nederpelt C, Mokhtari AK, Mashbari H, Tsiligkaridis T, Saillant NN. Am. J. Surg. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.amjsurg.2023.03.019

PMID

36948898

Abstract

BACKGROUND: Tiered trauma triage systems have resulted in a significant mortality reduction, but models have remained unchanged. The aim of this study was to develop and test an artificial intelligence algorithm to predict critical care resource utilization.

METHODS: We queried the ACS-TQIP 2017-18 database for truncal gunshot wounds(GSW). An information-aware deep neural network (DNN-IAD) model was trained to predict ICU admission and need for mechanical ventilation (MV). Input variables included demographics, comorbidities, vital signs, and external injuries. The model's performance was assessed using the area under receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC).

RESULTS: For the ICU admission analysis, we included 39,916 patients. For the MV need analysis, 39,591 patients were included. Median (IQR) age was 27 (22,36). AUROC and AUPRC for predicting ICU need were 84.8 ± 0.5 and 75.4 ± 0.5, and the AUROC and AUPRC for MV need were 86.8 ± 0.5 and 72.5 ± 0.6.

CONCLUSIONS: Our model predicts hospital utilization outcomes in patients with truncal GSW with high accuracy, allowing early resource mobilization and rapid triage decisions in hospitals with capacity issues and austere environments.

Language: en

Keywords

Triage; Machine learning; AI; Gunshot; Resource utilization